Currently, deep learning-based intelligent fault diagnosis techniques have been widely used in the manufacturing industry. However, due to various constraints, fault data for rotating machinery is often limited. Moreover, in real industrial environments, operating conditions of rotating machinery vary based on task requirements, leading to significant data variability across different operating conditions. This variability presents a major challenge for few-shot fault diagnosis, especially in scenarios requiring domain generalization across diverse operating conditions. To address this challenge, this paper proposes multiscale scattering forests (MSF): a domain-generalizing approach for fault diagnosis under data constraints. Firstly, a multiscale wavelet scattering predefined layer is designed to extract robust invariant features from input samples, where these scattering coefficients are concatenated and then used as new samples resulting from the data enhancement of the original samples. Then, a deep stacked ensemble forests with skip connection is designed to handle the transformed multiscale samples, allowing earlier information to jump over layers and improving the model’s feature representation capabilities. Finally, a similarity metric-based weighting learning strategy is developed to implement diagnostic results of each forest, integrating the models of assigning weights into an ensemble framework to enhance domain generalization performance under various operation conditions. The MSF model is comprehensively evaluated using a computer numerical control (CNC) machine tool spindle bearing dataset in an industrial environment. Experimental results demonstrate that the proposed approach not only exhibits strong diagnostic and generalization performance in few-shot scenarios without the support of additional source domains but also outperforms other state-of-the-art few-shot fault diagnosis methods.
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